plot.segmented: Plot method for segmented objects
In segmented: Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects)

plot.segmented

R Documentation

Plot method for segmented objects

Description

Takes a fitted segmented object returned by segmented() and plots (or adds) the fitted broken-line relationship for the selected segmented term.

Usage

## S3 method for class 'segmented'
plot(x, term, add=FALSE, res=FALSE, conf.level=0, interc=TRUE, link=TRUE, 
    res.col=grey(.15, alpha = .4), rev.sgn=FALSE, const=NULL, shade=FALSE, rug=!add, 
    dens.rug=FALSE, dens.col = grey(0.8), transf=I, isV=FALSE, is=FALSE, var.diff=FALSE, 
    p.df="p", .vcov=NULL, .coef=NULL, prev.trend=FALSE, smoos=NULL, hide.zeros=FALSE, 
    leg="topleft", psi.lines=FALSE, ...)

Arguments

`x`	a fitted `segmented` object.
`term`	Numerical or character to indicate the segmented variable having the piece-wise relationship to be plotted. If there is a single segmented variable in the fitted model `x`, `term` can be omitted. If vector, multiple segmented lines will be drawn on the same plot.
`add`	when `TRUE` the fitted lines are added to the current device.
`res`	when `TRUE` the fitted lines are plotted along with corresponding partial residuals. See Details.
`conf.level`	If greater than zero, it means the confidence level at which the pointwise confidence itervals have to be plotted.
`interc`	If `TRUE` the computed segmented components include the model intercept (if it exists).
`link`	when `TRUE` (default), the fitted lines are plotted on the link scale, otherwise they are tranformed on the response scale before plotting. Ignored for linear segmented fits.
`res.col`	when `res=TRUE` it means the color of the points representing the partial residuals.
`rev.sgn`	when `TRUE` it is assumed that current `term` is ‘minus’ the actual segmented variable, therefore the sign is reversed before plotting. This is useful when a null-constraint has been set on the last slope.
`const`	constant to add to each fitted segmented relationship (on the scale of the linear predictor) before plotting. If `const=NULL` and the fit includes a segmented interaction term (obtained via `seg(..,by)` in the formula), the group-specific intercept is included.
`shade`	if `TRUE` and `conf.level>0` it produces shaded regions (in grey color) for the pointwise confidence intervals embracing the fitted segmented line.
`rug`	when `TRUE` the covariate values are displayed as a rug plot at the foot of the plot. Default is to `!add`.
`dens.rug`	when `TRUE` then smooth covariate distribution is plotted on the x-axis.
`dens.col`	if `dens.rug=TRUE`, it means the colour to be used to plot the density.
`transf`	A possible function to convert the fitted values before plotting. It is only effective if `res=FALSE`. If `res=TRUE` any transformation is ignored.
`isV`	logical value (to be passed to `broken.line`). Ignored if `conf.level=0`
`is`	logical value (to be passed to `broken.line`) indicating if the covariance matrix based on the induced smoothing should be used. Ignored if `conf.level=0`
`var.diff`	logical value to be passed to `summary.segmented` to compute dthe standard errors of fitted values (if `conf.level>0`).
`p.df`	degrees of freedom when `var.diff=TRUE`, see `summary.segmented`
`.vcov`	The full covariance matrix of estimates to be used when `conf.level>0`. If unspecified (i.e. `NULL`), the covariance matrix is computed internally by the function `vcov.segmented`.
`.coef`	The regression parameter estimates. If unspecified (i.e. `NULL`), it is computed internally by `coef()`.
`prev.trend`	logical. If `TRUE` dashed lines corresponding to the ‘previous’ trends (i.e. the trends if the breakpoints would not have occurred) are also drawn.
`smoos`	logical, indicating if the residuals (provided that `res=TRUE`) will be drawn using a smoothed scatterplot. If `NULL` (default) the smoothed scatterplot will be employed when the number of observation is larger than 10000.
`hide.zeros`	logical, indicating if the residuals (provided that `res=TRUE`) corresponding to the covariate zero values should be deleted. Useful when the fit includes an interaction term in the formula, such as `seg(.., by=..)`, and the zeros in covariates indicate units in other groups.
`leg`	If the plot refers to segmented relationships in groups, i.e. `term` has been specified as a vector, a legend is placed at the specified `leg` position. Put `NA` not to draw the legend.
`psi.lines`	if `TRUE` vertical lines corresponding to the estimated breakpoints are also drawn. Ignored if `term` is not a vector.
`...`	other graphics parameters to pass to plotting commands: ‘col’, ‘lwd’ and ‘lty’ (that can be vectors and are recycled if necessary, see the example below) for the fitted piecewise lines; ‘ylab’, ‘xlab’, ‘main’, ‘sub’, ‘cex.axis’, ‘cex.lab’, ‘xlim’ and ‘ylim’ when a new plot is produced (i.e. when `add=FALSE`); ‘pch’ and ‘cex’ for the partial residuals (when `res=TRUE`, `res.col` is for the color); `col.shade` for the shaded regions (provided that `shade=TRUE` and `conf.level>0`).

Details

Produces (or adds to the current device) the fitted segmented relationship between the response and the selected term. If the fitted model includes just a single ‘segmented’ variable, term may be omitted.
The partial residuals are computed as ‘fitted + residuals’, where ‘fitted’ are the fitted values of the segmented relationship relevant to the covariate specified in term. Notice that for GLMs the residuals are the response residuals if link=FALSE and the working residuals if link=TRUE.

Value

None.

Note

For models with offset, partial residuals on the response scale are not defined. Thus plot.segmented does not work when link=FALSE, res=TRUE, and the fitted model includes an offset.
When term is a vector and multiple segmented relationships are being drawn on the same plot, col and res.col can be vectors. Also pch, cex, lty, and lwd can be vectors, if specified.

Author(s)

Vito M. R. Muggeo

Examples

set.seed(1234)
z<-runif(100)
y<-rpois(100,exp(2+1.8*pmax(z-.6,0)))
o<-glm(y~z,family=poisson)
o.seg<-segmented(o) #single segmented covariate and one breakpoint: 'seg.Z' and 'psi' can be omitted
par(mfrow=c(1,2))
plot(o.seg, conf.level=0.95, shade=TRUE)
points(o.seg, link=TRUE, col=2)
## new plot
plot(z,y)
## add the fitted lines using different colors and styles..
plot(o.seg,add=TRUE,link=FALSE,lwd=2,col=2:3, lty=c(1,3))
lines(o.seg,col=2,pch=19,bottom=FALSE,lwd=2) #for the CI for the breakpoint
points(o.seg,col=4, link=FALSE)
## using the options 'is', 'isV', 'shade' and 'col.shade'.
par(mfrow=c(1,2))
plot(o.seg, conf.level=.9, is=TRUE, isV=TRUE, col=1, shade = TRUE, col.shade=2)
plot(o.seg, conf.level=.9, is=TRUE, isV=FALSE, col=2, shade = TRUE, res=TRUE, res.col=4, pch=3)

segmented documentation built on Oct. 25, 2024, 5:07 p.m.